Projects per year
Abstract
Molecular Dynamic (MD) simulations are very effective in the discovery of nanomedicines for treating cancer, but these are computationally expensive and time-consuming. Existing studies integrating machine learning (ML) into MD simulation to enhance the process and enable efficient analysis cannot provide direct insights without the complete simulation. In this study, we present an ML-based approach for predicting the solvent accessible surface area (SASA) of a nanoparticle (NP), denoting its efficacy, from a fraction of the MD simulations data. The proposed framework uses a time series model for simulating the MD, resulting in an intermediate state, and a second model to calculate the SASA in that state. Empirically, the solution can predict the SASA value 260 timesteps ahead 7.5 times faster with a very low average error of 1956.93. We also introduce the use of an explainability technique to validate the predictions. This work can reduce the computational expense of both processing and data size greatly while providing reliable solutions for the nanomedicine design process.
Original language | English |
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Article number | 547 |
Journal | Scientific Reports |
Volume | 13 |
Issue number | 1 |
DOIs | |
Publication status | Published - Dec 2023 |
MoE publication type | A1 Journal article-refereed |
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EDISS: EMJMD Programme on the Engineering of Data-intensive Intelligent Software Systems
Lafond, S. (Principal Investigator), Azimi Rashti, S. (Principal Investigator), Lilius, J. (Co-Principal Investigator), Strömborg, M. (Coordinator) & Iancu, B. (Co-Investigator)
Education, Audiovisual and Culture Executive Agency - European Commission
01/09/20 → 31/08/26
Project: EU
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EVO-NANO: Evolvable platform for designing cancer treatment strategies using nanoparticles
Lafond, S. (Principal Investigator) & Azimi Rashti, S. (Co-Investigator)
01/10/18 → 31/03/22
Project: EU